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dc.contributor.authorYao, Lin
dc.contributor.authorChen, Mei Lin
dc.contributor.authorSheng, Xinjun
dc.contributor.authorMrachacz-Kersting, Natalie
dc.contributor.authorZhu, Xiangyang
dc.contributor.authorFarina, Dario
dc.contributor.authorJiang, Ning
dc.date.accessioned2017-09-06 17:16:02 (GMT)
dc.date.available2017-09-06 17:16:02 (GMT)
dc.date.issued2017-07-24
dc.identifier.urihttps://doi.org/10.1109/TNSRE.2017.2731261
dc.identifier.urihttp://hdl.handle.net/10012/12327
dc.description© 2017 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Yao, L., Chen, M. L., Sheng, X., Mrachacz-Kersting, N., Zhu, X., Farina, D., & Jiang, N. (2017). A multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamics. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1–1. https://doi.org/10.1109/TNSRE.2017.2731261en
dc.description.abstractWe proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L), 2) selective sensation on right hand (SS-R), 3) bilateral selective sensation (SS-B), and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9±8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3±4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had accuracy of 75.2±8.3%, with the best discrimination reached for the classes SS-L, SS-R and SS-S. Finally, four classes were classified with accuracy of 59.4±7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.en
dc.description.sponsorshipUniversity Starter Grant of the University of Waterloo || No. 203859 National Natural Science Foundation of China Grant No. || 51620105002, 51375296, 51421092 Research Project of State Key Laboratory of Mechanical System and Vibration || MSV201607en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectElectroencephalographyen
dc.subjectWristen
dc.subjectVibrationsen
dc.subjectTime-frequency analysisen
dc.subjectVisualizationen
dc.subjectBrain-computer interfacesen
dc.subjectIndexesen
dc.titleA multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamicsen
dc.typeArticleen
dcterms.bibliographicCitationYao, L., Chen, M. L., Sheng, X., Mrachacz-Kersting, N., Zhu, X., Farina, D., & Jiang, N. (2017). A multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamics. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1–1. https://doi.org/10.1109/TNSRE.2017.2731261en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Systems Design Engineeringen
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen


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